【論文閱讀筆記】Linguistic Knowledge and Transferability of Contextual Representations
本文發佈在arxiv 2019 preprint 通過十六種不同的探究任務來研究語境化知識和語境化詞語表示的可遷移性。預訓練的上下文相關詞向量足以在廣泛的NLP任務中實現高性能。對於需要特定信息但未被上下文單詞表示捕獲的任務，學習特定任務的上下文特徵有助於在詞向量中編碼必要的知識。此外，對情境化層的可遷移性模式的分析表明，LSTM的最低層編碼最具可遷移的特徵，而Transoform
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相關文章
- 1. 論文筆記《Knowledge Enhanced Contextual Word Representations》
- 2. 【論文閱讀】 VL-BERT: Pre-training of generic visual-linguistic representations
- 3. Distributed Representations of Sentences and Documents閱讀筆記
- 4. 論文閱讀：DeepWalk Online Learning of Social Representations
- 5. Distributed Representations of Words and Phrases and their Compositionality論文閱讀及實戰
- 6. Contextual Word Representations and Pretraining
- 7. [論文筆記]UNDERSTANDING AND ENHANCING THE TRANSFERABILITY OF ADVERSARIAL EXAMPLES(archive)
- 8. 【論文筆記-AAAI2020】Overcoming Language Priors in VQA via Decomposed Linguistic Representations
- 9. 論文閱讀筆記－CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases
- 10. Machine Learning & Deep Learning 論文閱讀筆記